Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

Image Betrayal Checking Based on the Digital Fingerprint of Images on the Internet

In the Internet environment, documents are easily leaked, and divulged files spread rapidly. Therefore, it is important for privacy institutions to actively check the documents on the Internet to find out whether some private files have been leaked. In this paper, we put forward a scheme for active image betrayal checking on the Intemet based on the digital fingerprint, which embeds fingerprints into privacy documents, extracts codes from the Intemet images, and then fmds out the divulged files by matching two groups of codes. Due to so many documents on the Internet, the number of times of code comparison is huge, which leads to a large running time. To overcome the deficiency in practical application, we optimized the process by accurate matching methods and approximate matching method. Then a method was proposed to group objects by locality sensitive hashing （LSH） process before code comparison, in order to eliminate the vast majority of unrelated pairs. Experiments prove that this method could operate with less running time and less memory.